Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters

Abstract

Abstract:The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρ w ) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. ρ w relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low-to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in ρ w retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries' monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color

Shellfish Aquaculture from Space: Potential of Sentinel2 to Monitor Tide-Driven Changes in Turbidity, Chlorophyll Concentration and Oyster Physiological Response at the Scale of an Oyster Farm

“…This algorithm has been validated for the Operational Land Imager (OLI) onboard Landsat8 (Novoa et al, 2017). A spectral recalibration has been performed here so that the algorithm could be applied to Sentinel2/MSI using bands B4 (665 nm) and B8A (865 nm).…”

“…For the SWIR algorithm, ACOLITE also provides a choice for aerosol correction using a fixed epsilon over the region of interest (ROI), or a per pixel variable epsilon. As the NIR AC option is not adapted to turbid waters (Vanhellemont and Ruddick, 2015), we used here the SWIR AC option with a fixed epsilon over the ROI, as recommended by several authors (Van der Zande et al, 2016;Novoa et al, 2017;Tristan Harmel, personal communication). The ROI was taken as Bourgneuf Bay and the Loire estuary (i.e., longitude from −2.35 to −1.95 • E, and latitude from 46.85 to 47.35 • N).…”

“…The SPM concentration was computed using a multi-conditional algorithm previously developed for Bourgneuf Bay and the Loire estuary (Novoa et al, 2017). This algorithm has been validated for the Operational Land Imager (OLI) onboard Landsat8 (Novoa et al, 2017).…”

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The algorithms of Novoa et al (2017) and Gons et al (2005) were recalibrated and applied to Sentinel2 data to retrieve suspended particulate matter (SPM) and chlorophyll a (chl a) concentration in the environmentally and economically important intertidal zones. Sentinel2-derived chl a and SPM concentration distributions were analyzed at the scale of an oyster farm over a variety of tidal conditions.

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Seasonal Distribution of Suspended Particulate Matter off China’s Subei Coast

“…In addition, the atmospheric correction methodology and equations developed [23] for AVHRR-visible data and the developed cloud detection method [24] were adopted. The satellite images processed by atmospheric correction and geoference reflect water-leaving reflectance, and waterleaving reflectance can be transformed to remote sensing reflectance following the procedure described by Doxaran [25]. Therefore, the surface SPM concentrations can be retrieved from satellite images based on the relationship between the Rrs and in situ SPM concentrations.…”